Two-Stage Neural Network Approach to Precise 24-Hour Load Pattern Prediction

  • Krzysztof Siwek
  • Stanislaw Osowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)


The paper presents the neural network approach to the precise 24-hour load pattern prediction for the next day in the power system. In this approach we use the ensemble of few neural network predictors working in parallel. The predicted series containing 24 values of the load pattern generated by the neural predictors are combined together using principal component analysis. Few principal components form the input vector for the final stage predictor composed of another neural network. The developed system of prediction was tested on the real data of the Polish Power System. The results have been compared to the appropriate values generated by other methods.


load forecasting neural networks PCA 


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  1. 1.
    Cottrell, M., Girard, B., Girard, Y., Muller, C., Rousset, P.: Daily electrical power curve: classification and forecasting using a Kohonen map. In: Sandoval, F., Mira, J. (eds.) IWANN 1995. LNCS, vol. 930, pp. 1107–1113. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  2. 2.
    Diamantras, K., Kung, S.Y.: Principal component neural networks. Wiley, N.Y (1996)Google Scholar
  3. 3.
    Fidalgo, J.N., Pecas Lopez, J.: Load forecasting performance enhancement when facing anomalous events. IEEE Trans. Power Systems 20, 408–415 (2005)CrossRefGoogle Scholar
  4. 4.
    Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona-Fernandez, D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power Systems 21, 1946–1953 (2006)CrossRefGoogle Scholar
  5. 5.
    Haykin, S.: Neural networks, a comprehensive foundation. Macmillan, N.Y (2002)zbMATHGoogle Scholar
  6. 6.
    Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. on Power Systems 16, 44–55 (2001)CrossRefGoogle Scholar
  7. 7.
    Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Electrical Power and Energy Systems 28, 525–530 (2006)CrossRefGoogle Scholar
  8. 8.
    Kuntcheva, L.: Combining pattern classifiers - methods and algorithms. Wiley, New Jersey (2004)CrossRefGoogle Scholar
  9. 9.
    Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T.: A neural network based several hours ahead electric load forecasting using similar days approach. Electrical Power and Energy Systems 28, 367–373 (2006)CrossRefGoogle Scholar
  10. 10.
    Osowski, S., Siwek, K.: The self-organizing neural network approach to load forecasting in power system. In: Int. Joint Conf. on Neural Networks, Washington, pp. 1345–1348 (1999)Google Scholar
  11. 11.
    Osowski, S., Siwek, K.: Regularization of neural networks for load forecasting in power system. In: IEE Proc. GTD, vol. 149, pp. 340–345 (2002)Google Scholar
  12. 12.
    Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)zbMATHGoogle Scholar
  13. 13.
    Sorjamaa, A., Hao, J., Reyhani, N., Li, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70, 2861–2869 (2007)CrossRefGoogle Scholar
  14. 14.
    Yalcinoz, T., Eminoglu, U.: Short term and medium term power distribution load forecasting by neural networks. Energy Conversion and Management 46, 1393–1405 (2005)CrossRefGoogle Scholar
  15. 15.
    Matlab manual, user’s guide, MathWorks, Natick (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Krzysztof Siwek
    • 1
  • Stanislaw Osowski
    • 1
    • 2
  1. 1.Dept. of Electrical EngineeringWarsaw University of TechnologyWarsawPoland
  2. 2.Dept. of ElectronicsMilitary University of TechnologyWarsawPoland

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